Video Transcoding Control via Complexity Signatures
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Solution Overview
Problem
Current Content Delivery Network (CDN) and Cloud Digital Video Recorder (CDVR) systems face challenges in managing transcoding complexity, leading to inefficiencies in handling variable video stream demands and quality adaptations, resulting in suboptimal user experience and increased costs due to the lack of a well-established decision model for purging or storing video content.
Innovation Solution
A method and system for dynamically controlling transcoding complexity by determining complexity signatures and requirements, comparing them to a threshold, and adapting control parameters such as transcoding segment duration and order, using transcoding hint signals and machine learning predictions to optimize transcoding operations and reduce peak load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If content is purged from storage after one week to reduce storage costs, then storage cost is reduced, but transcoding complexity increases when content is requested later
Solution Approach 1:
The system performs preliminary transcoding and stores multiple quality versions of content in advance. When content is requested, the system can directly deliver the appropriate quality version without performing complex just-in-time transcoding, thus reducing both storage costs (by selectively purging) and transcoding complexity (by pre-processing).
Solution Approach 2:
The system dynamically adjusts storage and transcoding strategies based on content popularity and access patterns. Frequently accessed content is retained in multiple qualities, while less popular content is purged and re-transcoded only when needed, optimizing the balance between storage cost and transcoding complexity over time.
2Device complexity
If CDN nodes are dimensioned for a limited number of streams, then device complexity is reduced, but adaptability to evolving demand decreases
Solution Approach 1:
The system implements dynamic resource allocation and load balancing that allows CDN nodes to adapt to varying stream demands in real-time. Transcoding resources are dynamically provisioned based on current load, enabling the system to handle evolving demand patterns without requiring over-provisioning for peak scenarios.
Solution Approach 2:
The system designs CDN nodes with multi-functional capabilities, where a single node can handle multiple stream types and qualities through software-based transcoding. This universal approach allows limited physical infrastructure to serve diverse and evolving content delivery needs without requiring dedicated hardware for each stream type.
3Reliability
If worst-case provisioning is used to manage multiple transcoding jobs, then reliability is improved, but productivity decreases
Solution Approach 1:
The system dynamically adjusts transcoding resource allocation based on actual job priorities, content characteristics, and system load. Instead of always provisioning for worst-case scenarios, the system adaptively scales resources to match actual demand, ensuring reliable completion of high-priority jobs while maintaining higher overall throughput by efficiently utilizing available capacity.
Solution Approach 2:
The system changes transcoding parameters such as bitrate, resolution, and codec settings based on content analysis and delivery requirements. This parameter optimization reduces transcoding complexity for many common cases while maintaining quality, allowing the system to achieve reliable job completion with higher productivity by avoiding unnecessary worst-case provisioning.
Data Source
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AI summary
A method for controlling transcoding of at least one video stream (Ref_V1) from a reference quality into at least one different quality; the method comprising: determining at least one respective complexity signature of the at least one video stream; determining complexity requirements for transcoding the at least one video stream from the reference quality into the at least one different quality, based on the determined at least one respective complexity signature; comparing the determined complexity requirements to a complexity threshold; and if it is found that the determined complexity requirements exceed the complexity threshold, adapting at least one control parameter (Search window, IQA, H2) of the transcoding.